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H Company releases NeoMME, a single-tower multimodal encoder family

By PulseAugur Editorial · [2 sources] ·

H Company has introduced NeoMME, a new family of multimodal encoders designed for efficiency. These models, available in 260M and 800M parameter sizes, eliminate the need for separate vision towers and causal decoders, processing both text and image data through a single transformer architecture. This streamlined approach aims to reduce computational overhead while maintaining strong performance, with the 260M NeoMME-Retriever achieving competitive results on document retrieval benchmarks. AI

IMPACT This new architecture could lead to more efficient multimodal AI systems by reducing computational overhead.

RANK_REASON Release of a new family of multimodal encoders with novel architecture.

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

H Company releases NeoMME, a single-tower multimodal encoder family

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Release of a new family of multimodal encoders with novel architecture.
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2 independent sources
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model release, infra
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High
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45 days old
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COVERAGE [2]

  1. MarkTechPost TIER_1 English(EN) · Asif Razzaq ·

    H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder

    <p>We look at NeoMME, a family of 260M and 800M bidirectional encoders from H Company. Unlike ColPali-style retrievers, it processes multilingual text tokens and raw 32×32 image patches in a single Transformer, with no pretrained vision tower and no causal decoder. We cover the m…

  2. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    Researchers at H Company have released NeoMME, a family of single-tower multimodal encoders with 260M and 800M parameters that discard the traditional vision to

    Researchers at H Company have released NeoMME, a family of single-tower multimodal encoders with 260M and 800M parameters that discard the traditional vision tower and causal decoder architecture. The approach aims to simplify multimodal AI while maintaining performance. https://…

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